TY - JOUR A1 - Dachwald, Bernd A1 - Wurm, Patrick T1 - Mission analysis and performance comparison for an Advanced Solar Photon Thruster JF - Advances in Space Research Y1 - 2011 SN - 0273-1177 VL - 48 IS - 11 SP - 1858 EP - 1868 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Bung, Daniel Bernhard ED - Rowinski, Pawel T1 - Laboratory models of free-surface flows T2 - Rivers - physical, fluvial and environmental processes N2 - Hydraulic modeling is the classical approach to investigate and describe complex fluid motion. Many empirical formulas in the literature used for the hydraulic design of river training measures and structures have been developed using experimental data from the laboratory. Although computer capacities have increased to a high level which allows to run complex numerical simulations on standard workstation nowadays, non-standard design of structures may still raise the need to perform physical model investigations. These investigations deliver insight into details of flow patterns and the effect of varying boundary conditions. Data from hydraulic model tests may be used for calibration of numerical models as well. As the field of hydraulic modeling is very complex, this chapter intends to give a short overview on capacities and limits of hydraulic modeling in regard to river flows and hydraulic structures only. The reader shall get a first idea of modeling principles and basic considerations. More detailed information can be found in the references. KW - Physical modeling KW - Similitude KW - Open channels KW - Hydraulic structures Y1 - 2015 SN - 978-3-319-17718-2 ; 978-3-319-17719-9 U6 - https://doi.org/10.1007/978-3-319-17719-9_9 SP - 213 EP - 228 PB - Springer CY - Cham ER - TY - CHAP A1 - Wu, Chunsheng A1 - Bronder, Thomas A1 - Poghossian, Arshak A1 - Schöning, Michael Josef T1 - DNA-hybridization detection using light-addressable potentiometric sensor modified with gold layer T2 - Sensoren und Messsysteme 2014 ; Beiträge der 17. GMA/ITG-Fachtagung vom 3. bis 4. Juni 2014 in Nürnberg. (ITG-Fachbericht ; 250) Y1 - 2014 SN - 978-3-8007-3622-5 SP - 1 EP - 4 PB - VDE-Verl. CY - Düsseldorf ER - TY - CHAP A1 - Huck, Christina A1 - Poghossian, Arshak A1 - Buniatyan, V. A1 - Schöning, Michael Josef T1 - Multi-parameter detection for supporting monitoring and control of biogas processes in agriculture T2 - Sensoren und Messsysteme 2014 ; Beiträge der 17. GMA/ITG-Fachtagung vom 3. bis 4. Juni 2014 in Nürnberg. (ITG-Fachbericht ; 250) Y1 - 2014 SN - 978-3-8007-3622-5 SP - 1 EP - 5 PB - VDE-Verl. CY - Berlin ER - TY - CHAP A1 - Valero, Daniel A1 - Bung, Daniel Bernhard T1 - Hybrid investigation of air transport processes in moderately sloped stepped spillway flows T2 - E-proceedings of the 36th IAHR World Congress 28 June – 3 July, 2015, The Hague, the Netherlands Y1 - 2015 SP - 1 EP - 10 ER - TY - CHAP A1 - Schöning, Michael Josef A1 - Poghossian, Arshak A1 - Glück, Olaf A1 - Thust, Marion T1 - Electrochemical methods for the determination of chemical variables in aqueous media T2 - Measurement, instrumentation, and sensors handbook / ed. by John G. Webster [u.a.] Vol. 2 : Electromagnetic, optical, radiation, chemical, and biomedical measurement Y1 - 2014 SN - 978-1-4398-4891-3 SP - 55-1 EP - 55-54 PB - CRC Pr. CY - Boca Raton, Fla. ER - TY - JOUR A1 - Panc, Nicolae A1 - Contiu, Glad A1 - Bocanet, Vlad A1 - Thurn, Laura A1 - Sabau, Emilia T1 - The influence of cutting technology on surface wear hardness JF - Academic Journal of Manufacturing Engineering Y1 - 2019 SN - 1583-7904 VL - 17 IS - 3 SP - 205 EP - 210 ER - TY - CHAP A1 - Chanson, Hubert A1 - Bung, Daniel Bernhard A1 - Matos, J. T1 - Stepped spillways and cascades T2 - Energy dissipation in hydraulic structures / Hubert Chanson (ed.) Y1 - 2015 SN - 978-1-138-02755-8 (print) ; 978-1-315-68029-3 (e-Book) SP - 45 EP - 64 PB - CRC Press CY - Boca Raton, Fla. [u.a.] ER - TY - JOUR A1 - Schiffer, Stefan A1 - Ferrein, Alexander T1 - ERIKA—Early Robotics Introduction at Kindergarten Age JF - Multimodal Technologies Interact N2 - In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human–robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents. Y1 - 2018 U6 - https://doi.org/10.3390/mti2040064 SN - 2414-4088 VL - 2 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Schwabedal, Justus T. C. A1 - Sippel, Daniel A1 - Brandt, Moritz D. A1 - Bialonski, Stephan T1 - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning N2 - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle. Y1 - 2018 U6 - https://doi.org/10.48550/arXiv.1809.08443 ER -